The present invention relates generally to the customization of collected data in a data processing system. More particularly, the invention relates to dynamically modifying criteria used by monitoring software.
The Web is increasingly becoming an interactive data exchange forum, where users submit content to Web servers, which is shared with other users, sometimes after processing. This evolution of the World Wide Web is sometimes referred to as Web 2.0, wherein applications promote collaboration and information sharing among large groups of users. Traditionally, owners of Web servers have competed for consumer attention by providing customer desired offerings, for which they are financially rewarded through advertising revenue, goodwill, lower customer service costs, resale of metrics of usage patterns, usage fees, and the like. A fundamental paradigm of Web 2.0 is to grant end-users (those users other than the originator) the privileges to read/write/update data, meter transactions, monitor usage, dynamically apply “what if's” to the collected data and otherwise monetize value of existing applications and data—tasks traditionally reserved to application developers.
Web services add a further complication to this already dynamic environment. Web services are independent software modules adhering to known standards, such as those published by the World Wide Web consortium (W3C). Web services are often created by third party developers, which are integrated into Web based offerings of other vendors that enhance a functionality of these offerings. In addition to Web services, other software assets have emerged that represent enhancements to offerings of other vendors. These assets include, for example, Web content, widgets, mashups, service oriented architecture (SOA) applications, and the like.
In many situations, a single solution utilized by a user is formed from many assets by multiple different providers. The solution itself can represent a remixing of content and services by the providers themselves or by middlemen into solutions that are consumed by users as a single view. Additionally, services and content can vary in granularity. Some can be fine grained, while others can be delivered in bulk. For example, business information can be tailored for delivery to a single company compared to delivering business information to a set of one thousand companies (e.g., Dun and Bradstreet business information).
Increasingly, an issue of providing a unified view of dissimilar data is arising. Gathering any data is linked to the challenge of determining what specific facts, statistics or items of information are to be monitored, stored and tracked. Representing a unified view of dissimilar data is likewise linked to the challenge of determining the relative importance, or weighting, each set of data should be afforded with respect to all other data in the collective representation. What's more, representing an updated view of the data in a “what if” scenario, where the relative weighting of the data is adjusted, presents a further challenge.